Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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With recent advances in machine vision, automatic detection of human expressions in video is becoming important especially because human labeling of videos is both tedious and error prone. In this paper, we present an approach for detecting facial expressions based on the Facial Action Coding System (FACS) in spontaneous videos. We present an automated system for detecting asymmetric eye open (AU41) and eye closed (AU43) actions. We use Gabor Jets to select distinctive features from the image and compare between three different classifiers--Bayesian networks, Dynamic Bayesian networks and Support Vector Machines--for classification. Experimental evaluation on a large corpus of spontaneous videos yielded an average accuracy of 98% for eye closed (AU43), and 92.75% for eye open (AU41).